#!/usr/bin/env python
# -*- coding: utf-8 -*-
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
Image tools interfaces
~~~~~~~~~~~~~~~~~~~~~~
"""
import os
import numpy as np
import nibabel as nb
import nilearn.image as nli
from niworkflows.nipype import logging
from niworkflows.nipype.utils.filemanip import fname_presuffix
from niworkflows.nipype.interfaces.base import (
traits, TraitedSpec, BaseInterfaceInputSpec,
File, InputMultiPath, OutputMultiPath)
from niworkflows.nipype.interfaces import fsl
from niworkflows.nipype.interfaces.base import SimpleInterface
LOGGER = logging.getLogger('interface')
class IntraModalMergeInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiPath(File(exists=True), mandatory=True,
desc='input files')
hmc = traits.Bool(True, usedefault=True)
zero_based_avg = traits.Bool(True, usedefault=True)
to_ras = traits.Bool(True, usedefault=True)
class IntraModalMergeOutputSpec(TraitedSpec):
out_file = File(exists=True, desc='merged image')
out_avg = File(exists=True, desc='average image')
out_mats = OutputMultiPath(File(exists=True), desc='output matrices')
out_movpar = OutputMultiPath(File(exists=True), desc='output movement parameters')
class IntraModalMerge(SimpleInterface):
input_spec = IntraModalMergeInputSpec
output_spec = IntraModalMergeOutputSpec
def _run_interface(self, runtime):
in_files = self.inputs.in_files
if not isinstance(in_files, list):
in_files = [self.inputs.in_files]
# Generate output average name early
self._results['out_avg'] = fname_presuffix(self.inputs.in_files[0],
suffix='_avg', newpath=runtime.cwd)
if self.inputs.to_ras:
in_files = [reorient(inf) for inf in in_files]
if len(in_files) == 1:
filenii = nb.load(in_files[0])
filedata = filenii.get_data()
# magnitude files can have an extra dimension empty
if filedata.ndim == 5:
sqdata = np.squeeze(filedata)
if sqdata.ndim == 5:
raise RuntimeError('Input image (%s) is 5D' % in_files[0])
else:
in_files = [fname_presuffix(in_files[0], suffix='_squeezed',
newpath=runtime.cwd)]
nb.Nifti1Image(sqdata, filenii.get_affine(),
filenii.get_header()).to_filename(in_files[0])
if np.squeeze(nb.load(in_files[0]).get_data()).ndim < 4:
self._results['out_file'] = in_files[0]
self._results['out_avg'] = in_files[0]
# TODO: generate identity out_mats and zero-filled out_movpar
return runtime
in_files = in_files[0]
else:
magmrg = fsl.Merge(dimension='t', in_files=self.inputs.in_files)
in_files = magmrg.run().outputs.merged_file
mcflirt = fsl.MCFLIRT(cost='normcorr', save_mats=True, save_plots=True,
ref_vol=0, in_file=in_files)
mcres = mcflirt.run()
self._results['out_mats'] = mcres.outputs.mat_file
self._results['out_movpar'] = mcres.outputs.par_file
self._results['out_file'] = mcres.outputs.out_file
hmcnii = nb.load(mcres.outputs.out_file)
hmcdat = hmcnii.get_data().mean(axis=3)
if self.inputs.zero_based_avg:
hmcdat -= hmcdat.min()
nb.Nifti1Image(
hmcdat, hmcnii.get_affine(), hmcnii.get_header()).to_filename(
self._results['out_avg'])
return runtime
CONFORMATION_TEMPLATE = """\t\t<h3 class="elem-title">Anatomical Conformation</h3>
\t\t<ul class="elem-desc">
\t\t\t<li>Input T1w images: {n_t1w}</li>
\t\t\t<li>Output orientation: RAS</li>
\t\t\t<li>Output dimensions: {dims}</li>
\t\t\t<li>Output voxel size: {zooms}</li>
\t\t\t<li>Discarded images: {n_discards}</li>
{discard_list}
\t\t</ul>
"""
DISCARD_TEMPLATE = """\t\t\t\t<li><abbr title="{path}">{basename}</abbr></li>"""
class TemplateDimensionsInputSpec(BaseInterfaceInputSpec):
t1w_list = InputMultiPath(File(exists=True), mandatory=True, desc='input T1w images')
max_scale = traits.Float(3.0, usedefault=True,
desc='Maximum scaling factor in images to accept')
class TemplateDimensionsOutputSpec(TraitedSpec):
t1w_valid_list = OutputMultiPath(exists=True, desc='valid T1w images')
target_zooms = traits.Tuple(traits.Float, traits.Float, traits.Float,
desc='Target zoom information')
target_shape = traits.Tuple(traits.Int, traits.Int, traits.Int,
desc='Target shape information')
out_report = File(exists=True, desc='conformation report')
class TemplateDimensions(SimpleInterface):
"""
Finds template target dimensions for a series of T1w images, filtering low-resolution images,
if necessary.
Along each axis, the minimum voxel size (zoom) and the maximum number of voxels (shape) are
found across images.
The ``max_scale`` parameter sets a bound on the degree of up-sampling performed.
By default, an image with a voxel size greater than 3x the smallest voxel size
(calculated separately for each dimension) will be discarded.
To select images that require no scaling (i.e. all have smallest voxel sizes),
set ``max_scale=1``.
"""
input_spec = TemplateDimensionsInputSpec
output_spec = TemplateDimensionsOutputSpec
def _generate_segment(self, discards, dims, zooms):
items = [DISCARD_TEMPLATE.format(path=path, basename=os.path.basename(path))
for path in discards]
discard_list = '\n'.join(["\t\t\t<ul>"] + items + ['\t\t\t</ul>']) if items else ''
zoom_fmt = '{:.02g}mm x {:.02g}mm x {:.02g}mm'.format(*zooms)
return CONFORMATION_TEMPLATE.format(n_t1w=len(self.inputs.t1w_list),
dims='x'.join(map(str, dims)),
zooms=zoom_fmt,
n_discards=len(discards),
discard_list=discard_list)
def _run_interface(self, runtime):
# Load images, orient as RAS, collect shape and zoom data
in_names = np.array(self.inputs.t1w_list)
orig_imgs = np.vectorize(nb.load)(in_names)
reoriented = np.vectorize(nb.as_closest_canonical)(orig_imgs)
all_zooms = np.array([img.header.get_zooms()[:3] for img in reoriented])
all_shapes = np.array([img.shape[:3] for img in reoriented])
# Identify images that would require excessive up-sampling
valid = np.ones(all_zooms.shape[0], dtype=bool)
while valid.any():
target_zooms = all_zooms[valid].min(axis=0)
scales = all_zooms[valid] / target_zooms
if np.all(scales < self.inputs.max_scale):
break
valid[valid] ^= np.any(scales == scales.max(), axis=1)
# Ignore dropped images
valid_fnames = in_names[valid]
self._results['t1w_valid_list'] = valid_fnames.tolist()
# Set target shape information
target_zooms = all_zooms[valid].min(axis=0)
target_shape = all_shapes[valid].max(axis=0)
self._results['target_zooms'] = tuple(target_zooms.tolist())
self._results['target_shape'] = tuple(target_shape.tolist())
# Create report
dropped_images = in_names[~valid]
segment = self._generate_segment(dropped_images, target_shape, target_zooms)
out_report = os.path.join(runtime.cwd, 'report.html')
with open(out_report, 'w') as fobj:
fobj.write(segment)
self._results['out_report'] = out_report
return runtime
class ConformInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc='Input image')
target_zooms = traits.Tuple(traits.Float, traits.Float, traits.Float,
desc='Target zoom information')
target_shape = traits.Tuple(traits.Int, traits.Int, traits.Int,
desc='Target shape information')
class ConformOutputSpec(TraitedSpec):
out_file = File(exists=True, desc='Conformed image')
transform = File(exists=True, desc='Conformation transform')
class ReorientInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True,
desc='Input T1w image')
class ReorientOutputSpec(TraitedSpec):
out_file = File(exists=True, desc='Reoriented T1w image')
transform = File(exists=True, desc='Reorientation transform')
class Reorient(SimpleInterface):
"""Reorient a T1w image to RAS (left-right, posterior-anterior, inferior-superior)
Syncs qform and sform codes for consistent treatment by all software
"""
input_spec = ReorientInputSpec
output_spec = ReorientOutputSpec
def _run_interface(self, runtime):
# Load image, orient as RAS
fname = self.inputs.in_file
orig_img = nb.load(fname)
reoriented = nb.as_closest_canonical(orig_img)
# Reconstruct transform from orig to reoriented image
ornt_xfm = nb.orientations.inv_ornt_aff(
nb.io_orientation(orig_img.affine), orig_img.shape)
normalized = normalize_xform(reoriented)
# Image may be reoriented
if normalized is not orig_img:
out_name = fname_presuffix(fname, suffix='_ras', newpath=runtime.cwd)
normalized.to_filename(out_name)
else:
out_name = fname
mat_name = fname_presuffix(fname, suffix='.mat', newpath=runtime.cwd, use_ext=False)
np.savetxt(mat_name, ornt_xfm, fmt='%.08f')
self._results['out_file'] = out_name
self._results['transform'] = mat_name
return runtime
class ValidateImageInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc='input image')
class ValidateImageOutputSpec(TraitedSpec):
out_file = File(exists=True, desc='validated image')
out_report = File(exists=True, desc='HTML segment containing warning')
class ValidateImage(SimpleInterface):
input_spec = ValidateImageInputSpec
output_spec = ValidateImageOutputSpec
def _run_interface(self, runtime):
img = nb.load(self.inputs.in_file)
out_report = os.path.abspath('report.html')
qform_code = img.header._structarr['qform_code']
sform_code = img.header._structarr['sform_code']
# Valid affine information
if (qform_code, sform_code) != (0, 0):
self._results['out_file'] = self.inputs.in_file
open(out_report, 'w').close()
self._results['out_report'] = out_report
return runtime
out_fname = fname_presuffix(self.inputs.in_file, suffix='_valid', newpath=runtime.cwd)
# Nibabel derives a default LAS affine from the shape and zooms
# Use scanner xform code to indicate no alignment has been done
img.set_sform(img.affine, nb.nifti1.xform_codes['scanner'])
img.to_filename(out_fname)
self._results['out_file'] = out_fname
snippet = (r'<h3 class="elem-title">WARNING - Invalid header</h3>',
r'<p class="elem-desc">Input file does not have valid qform or sform matrix.',
r'A default, LAS-oriented affine has been constructed.',
r'A left-right flip may have occurred.',
r'Analyses of this dataset MAY BE INVALID.</p>')
with open(out_report, 'w') as fobj:
fobj.write('\n'.join('\t' * 3 + line for line in snippet))
fobj.write('\n')
self._results['out_report'] = out_report
return runtime
class InvertT1wInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True,
desc='Skull-stripped T1w structural image')
ref_file = File(exists=True, mandatory=True,
desc='Skull-stripped reference image')
class InvertT1wOutputSpec(TraitedSpec):
out_file = File(exists=True, desc='Inverted T1w structural image')
class InvertT1w(SimpleInterface):
input_spec = InvertT1wInputSpec
output_spec = InvertT1wOutputSpec
def _run_interface(self, runtime):
t1_img = nli.load_img(self.inputs.in_file)
t1_data = t1_img.get_data()
epi_data = nli.load_img(self.inputs.ref_file).get_data()
# We assume the image is already masked
mask = t1_data > 0
t1_min, t1_max = np.unique(t1_data)[[1, -1]]
epi_min, epi_max = np.unique(epi_data)[[1, -1]]
scale_factor = (epi_max - epi_min) / (t1_max - t1_min)
inv_data = mask * ((t1_max - t1_data) * scale_factor + epi_min)
out_file = fname_presuffix(self.inputs.in_file, suffix='_inv', newpath=runtime.cwd)
nli.new_img_like(t1_img, inv_data, copy_header=True).to_filename(out_file)
self._results['out_file'] = out_file
return runtime
def reorient(in_file, out_file=None):
"""Reorient Nifti files to RAS"""
if out_file is None:
out_file = fname_presuffix(in_file, suffix='_ras', newpath=os.getcwd())
nb.as_closest_canonical(nb.load(in_file)).to_filename(out_file)
return out_file
def _flatten_split_merge(in_files):
if isinstance(in_files, str):
in_files = [in_files]
nfiles = len(in_files)
all_nii = []
for fname in in_files:
nii = nb.squeeze_image(nb.load(fname))
if nii.get_data().ndim > 3:
all_nii += nb.four_to_three(nii)
else:
all_nii.append(nii)
if len(all_nii) == 1:
LOGGER.warning('File %s cannot be split', all_nii[0])
return in_files[0], in_files
if len(all_nii) == nfiles:
flat_split = in_files
else:
splitname = fname_presuffix(in_files[0], suffix='_split%04d', newpath=os.getcwd())
flat_split = []
for i, nii in enumerate(all_nii):
flat_split.append(splitname % i)
nii.to_filename(flat_split[-1])
# Only one 4D file was supplied
if nfiles == 1:
merged = in_files[0]
else:
# More that one in_files - need merge
merged = fname_presuffix(in_files[0], suffix='_merged', newpath=os.getcwd())
nb.concat_images(all_nii).to_filename(merged)
return merged, flat_split
def extract_wm(in_seg, wm_label=3):
import os.path as op
import nibabel as nb
import numpy as np
nii = nb.load(in_seg)
data = np.zeros(nii.shape, dtype=np.uint8)
data[nii.get_data() == wm_label] = 1
hdr = nii.header.copy()
hdr.set_data_dtype(np.uint8)
nb.Nifti1Image(data, nii.affine, hdr).to_filename('wm.nii.gz')
return op.abspath('wm.nii.gz')
def normalize_xform(img):
""" Set identical, valid qform and sform matrices in an image
Selects the best available affine (sform > qform > shape-based), and
coerces it to be qform-compatible (no shears).
The resulting image represents this same affine as both qform and sform,
and is marked as NIFTI_XFORM_ALIGNED_ANAT, indicating that it is valid,
not aligned to template, and not necessarily preserving the original
coordinates.
If header would be unchanged, returns input image.
"""
# Let nibabel convert from affine to quaternions, and recover xform
tmp_header = img.header.copy()
tmp_header.set_qform(img.affine)
xform = tmp_header.get_qform()
xform_code = 2
# Check desired codes
qform, qform_code = img.get_qform(coded=True)
sform, sform_code = img.get_sform(coded=True)
if all((np.allclose(qform, xform), np.allclose(sform, xform),
int(qform_code) == xform_code, int(sform_code) == xform_code)):
return img
new_img = img.__class__(img.get_data(), xform, img.header)
# Unconditionally set sform/qform
new_img.set_sform(xform, xform_code)
new_img.set_qform(xform, xform_code)
return new_img